Solving energy management of renewable integrated microgrid systems using crow search algorithm
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This paper aims to percolate energy management of microgrid systems by minimizing the generation cost of the same. Energy management of microgrid refers to the optimal sizing and scheduling of the distributed energy resources to reduce the generation cost and pollutant emission. A recently developed crow search algorithm (CSA) is implemented to execute the optimization. The proposed CSA imitates the crows’ memory and tactics of hiding and chasing their food. Six renewable integrated microgrid test systems and a total of eighteen different cases are considered for this study. Various practical complexities such as valve point loading effect, combined economic–emission dispatch using price penalty factor method, modeling of the renewable energy sources and energy storage systems are taken into consideration for energy management of the microgrid systems. Results obtained are then compared to a number of different soft computing techniques such as genetic algorithm and particle swarm optimization and the likes to justify the effectiveness of the proposed algorithm. A statistical analysis, viz. Wilcoxon signed-rank test, is performed to prove the superiority of the proposed approach over the various other optimization techniques used in the paper.
KeywordsCombined economic–emission dispatch Penalty factor Microgrid Grey wolf optimization Teaching–learning-based optimization Sine cosine algorithm Crow search algorithm
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Conflict of interest
The authors declare that they have no conflicts of interest.
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This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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